Overview

Dataset statistics

Number of variables9
Number of observations768
Missing cells652
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Warnings

BloodPressure has 35 (4.6%) missing values Missing
SkinThickness has 227 (29.6%) missing values Missing
Insulin has 374 (48.7%) missing values Missing
BMI has 11 (1.4%) missing values Missing
Pregnancies has 111 (14.5%) zeros Zeros

Reproduction

Analysis started2021-02-17 02:23:36.692228
Analysis finished2021-02-17 02:23:56.247574
Duration19.56 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.845052083
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Memory size6.1 KiB
2021-02-17T02:23:56.357779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.369578063
Coefficient of variation (CV)0.8763413316
Kurtosis0.1592197775
Mean3.845052083
Median Absolute Deviation (MAD)2
Skewness0.9016739792
Sum2953
Variance11.35405632
MonotocityNot monotonic
2021-02-17T02:23:56.547519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1135
17.6%
0111
14.5%
2103
13.4%
375
9.8%
468
8.9%
557
7.4%
650
 
6.5%
745
 
5.9%
838
 
4.9%
928
 
3.6%
Other values (7)58
7.6%
ValueCountFrequency (%)
0111
14.5%
1135
17.6%
2103
13.4%
375
9.8%
468
8.9%
ValueCountFrequency (%)
171
 
0.1%
151
 
0.1%
142
 
0.3%
1310
1.3%
129
1.2%

Glucose
Real number (ℝ≥0)

Distinct135
Distinct (%)17.7%
Missing5
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean121.6867628
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-02-17T02:23:56.746635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199
median117
Q3141
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.53564107
Coefficient of variation (CV)0.2509364238
Kurtosis-0.2770397069
Mean121.6867628
Median Absolute Deviation (MAD)20
Skewness0.5309885349
Sum92847
Variance932.4253757
MonotocityNot monotonic
2021-02-17T02:23:56.929239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10017
 
2.2%
9917
 
2.2%
11114
 
1.8%
10614
 
1.8%
12914
 
1.8%
12514
 
1.8%
10513
 
1.7%
9513
 
1.7%
11213
 
1.7%
10213
 
1.7%
Other values (125)621
80.9%
ValueCountFrequency (%)
441
0.1%
561
0.1%
572
0.3%
611
0.1%
621
0.1%
ValueCountFrequency (%)
1991
 
0.1%
1981
 
0.1%
1974
0.5%
1963
0.4%
1952
0.3%

BloodPressure
Real number (ℝ≥0)

MISSING

Distinct46
Distinct (%)6.3%
Missing35
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean72.40518417
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-02-17T02:23:57.129683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile92
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.38215821
Coefficient of variation (CV)0.1710120394
Kurtosis0.9111578979
Mean72.40518417
Median Absolute Deviation (MAD)8
Skewness0.1341527317
Sum53073
Variance153.3178419
MonotocityNot monotonic
2021-02-17T02:23:57.316736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
7057
 
7.4%
7452
 
6.8%
7845
 
5.9%
6845
 
5.9%
7244
 
5.7%
6443
 
5.6%
8040
 
5.2%
7639
 
5.1%
6037
 
4.8%
6234
 
4.4%
Other values (36)297
38.7%
(Missing)35
 
4.6%
ValueCountFrequency (%)
241
 
0.1%
302
0.3%
381
 
0.1%
401
 
0.1%
444
0.5%
ValueCountFrequency (%)
1221
 
0.1%
1141
 
0.1%
1103
0.4%
1082
0.3%
1063
0.4%

SkinThickness
Real number (ℝ≥0)

MISSING

Distinct50
Distinct (%)9.2%
Missing227
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean29.15341959
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-02-17T02:23:57.497291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13
Q122
median29
Q336
95-th percentile46
Maximum99
Range92
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.47698237
Coefficient of variation (CV)0.3593740465
Kurtosis2.935491262
Mean29.15341959
Median Absolute Deviation (MAD)7
Skewness0.690619014
Sum15772
Variance109.7671596
MonotocityNot monotonic
2021-02-17T02:23:57.679869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3231
 
4.0%
3027
 
3.5%
2723
 
3.0%
2322
 
2.9%
3320
 
2.6%
2820
 
2.6%
1820
 
2.6%
3119
 
2.5%
1918
 
2.3%
3918
 
2.3%
Other values (40)323
42.1%
(Missing)227
29.6%
ValueCountFrequency (%)
72
 
0.3%
82
 
0.3%
105
0.7%
116
0.8%
127
0.9%
ValueCountFrequency (%)
991
0.1%
631
0.1%
601
0.1%
561
0.1%
542
0.3%

Insulin
Real number (ℝ≥0)

MISSING

Distinct185
Distinct (%)47.0%
Missing374
Missing (%)48.7%
Infinite0
Infinite (%)0.0%
Mean155.5482234
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-02-17T02:23:57.864797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile41.65
Q176.25
median125
Q3190
95-th percentile395.5
Maximum846
Range832
Interquartile range (IQR)113.75

Descriptive statistics

Standard deviation118.7758552
Coefficient of variation (CV)0.7635950616
Kurtosis6.370521815
Mean155.5482234
Median Absolute Deviation (MAD)55
Skewness2.166463844
Sum61286
Variance14107.70378
MonotocityNot monotonic
2021-02-17T02:23:57.998125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10511
 
1.4%
1409
 
1.2%
1309
 
1.2%
1208
 
1.0%
947
 
0.9%
1007
 
0.9%
1807
 
0.9%
1106
 
0.8%
1356
 
0.8%
1156
 
0.8%
Other values (175)318
41.4%
(Missing)374
48.7%
ValueCountFrequency (%)
141
0.1%
151
0.1%
161
0.1%
182
0.3%
221
0.1%
ValueCountFrequency (%)
8461
0.1%
7441
0.1%
6801
0.1%
6001
0.1%
5791
0.1%

BMI
Real number (ℝ≥0)

MISSING

Distinct247
Distinct (%)32.6%
Missing11
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean32.45746367
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-02-17T02:23:58.135003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.2
Q127.5
median32.3
Q336.6
95-th percentile44.5
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.924988332
Coefficient of variation (CV)0.2133558063
Kurtosis0.8633790278
Mean32.45746367
Median Absolute Deviation (MAD)4.6
Skewness0.5939697506
Sum24570.3
Variance47.9554634
MonotocityNot monotonic
2021-02-17T02:23:58.277305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3213
 
1.7%
31.612
 
1.6%
31.212
 
1.6%
33.310
 
1.3%
32.410
 
1.3%
32.89
 
1.2%
30.89
 
1.2%
32.99
 
1.2%
30.19
 
1.2%
29.78
 
1.0%
Other values (237)656
85.4%
(Missing)11
 
1.4%
ValueCountFrequency (%)
18.23
0.4%
18.41
 
0.1%
19.11
 
0.1%
19.31
 
0.1%
19.41
 
0.1%
ValueCountFrequency (%)
67.11
0.1%
59.41
0.1%
57.31
0.1%
551
0.1%
53.21
0.1%

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763021
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-02-17T02:23:58.750769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.331328595
Coefficient of variation (CV)0.7021513764
Kurtosis5.594953528
Mean0.4718763021
Median Absolute Deviation (MAD)0.1675
Skewness1.919911066
Sum362.401
Variance0.1097786379
MonotocityNot monotonic
2021-02-17T02:23:58.953166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2546
 
0.8%
0.2586
 
0.8%
0.2595
 
0.7%
0.2385
 
0.7%
0.2075
 
0.7%
0.2685
 
0.7%
0.2615
 
0.7%
0.1674
 
0.5%
0.194
 
0.5%
0.274
 
0.5%
Other values (507)719
93.6%
ValueCountFrequency (%)
0.0781
0.1%
0.0841
0.1%
0.0852
0.3%
0.0882
0.3%
0.0891
0.1%
ValueCountFrequency (%)
2.421
0.1%
2.3291
0.1%
2.2881
0.1%
2.1371
0.1%
1.8931
0.1%

Age
Real number (ℝ≥0)

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.24088542
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-02-17T02:23:59.166748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.76023154
Coefficient of variation (CV)0.3537881556
Kurtosis0.6431588885
Mean33.24088542
Median Absolute Deviation (MAD)7
Skewness1.129596701
Sum25529
Variance138.3030459
MonotocityNot monotonic
2021-02-17T02:23:59.355674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2272
 
9.4%
2163
 
8.2%
2548
 
6.2%
2446
 
6.0%
2338
 
4.9%
2835
 
4.6%
2633
 
4.3%
2732
 
4.2%
2929
 
3.8%
3124
 
3.1%
Other values (42)348
45.3%
ValueCountFrequency (%)
2163
8.2%
2272
9.4%
2338
4.9%
2446
6.0%
2548
6.2%
ValueCountFrequency (%)
811
0.1%
721
0.1%
701
0.1%
692
0.3%
681
0.1%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0500
65.1%
1268
34.9%
2021-02-17T02:23:59.723204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-17T02:23:59.835298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring characters

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number768
100.0%

Most frequent character per category

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common768
100.0%

Most frequent character per script

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII768
100.0%

Most frequent character per block

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Interactions

2021-02-17T02:23:46.392064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:46.709722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:46.839420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:46.955598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:47.066776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:47.198261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:47.361847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:47.492486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:48.655145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:48.806414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:48.955403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:49.077851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:49.249172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:49.448058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:49.624558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:49.777725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:49.921007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.033537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.150003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.274532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.399669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.526670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.648789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.763888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.875759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:50.979744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.085710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.199183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.314791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.434658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.545961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.646942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.755456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.852093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:51.963183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.069455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.195906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.318872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.439171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.548986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.649735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.779188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:52.908506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:53.066849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:53.395010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:53.591347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:53.711231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:53.826823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:53.959048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:54.087949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:54.235965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:54.373997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:54.508068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:54.629240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:54.760929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-17T02:23:54.897749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-17T02:23:59.928930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-17T02:24:00.270257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-17T02:24:00.485145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-17T02:24:00.673930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-17T02:23:55.192886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-17T02:23:55.539722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-17T02:23:55.894279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-17T02:23:56.072628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06148.072.035.0NaN33.60.627501
1185.066.029.0NaN26.60.351310
28183.064.0NaNNaN23.30.672321
3189.066.023.094.028.10.167210
40137.040.035.0168.043.12.288331
55116.074.0NaNNaN25.60.201300
6378.050.032.088.031.00.248261
710115.0NaNNaNNaN35.30.134290
82197.070.045.0543.030.50.158531
98125.096.0NaNNaNNaN0.232541

Last rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581106.076.0NaNNaN37.50.197260
7596190.092.0NaNNaN35.50.278661
760288.058.026.016.028.40.766220
7619170.074.031.0NaN44.00.403431
762989.062.0NaNNaN22.50.142330
76310101.076.048.0180.032.90.171630
7642122.070.027.0NaN36.80.340270
7655121.072.023.0112.026.20.245300
7661126.060.0NaNNaN30.10.349471
767193.070.031.0NaN30.40.315230